Literature DB >> 29684498

Multi-modal characterization of vasculature and nanoparticle accumulation in five tumor xenograft models.

Einar Sulheim1, Jana Kim2, Annemieke van Wamel3, Eugene Kim4, Sofie Snipstad3, Igor Vidic3, Ingeborg Hovde Grimstad3, Marius Widerøe4, Sverre H Torp5, Steinar Lundgren6, David J Waxman7, Catharina de Lange Davies3.   

Abstract

Preclinical research has demonstrated that nanoparticles and macromolecules can accumulate in solid tumors due to the enhanced permeability and retention effect. However, drug loaded nanoparticles often fail to show increased efficacy in clinical trials. A better understanding of how tumor heterogeneity affects nanoparticle accumulation could help elucidate this discrepancy and help in patient selection for nanomedicine therapy. Here we studied five human tumor models with varying morphology and evaluated the accumulation of 100 nm polystyrene nanoparticles. Each tumor model was characterized in vivo using micro-computed tomography, contrast-enhanced ultrasound and diffusion-weighted and dynamic contrast-enhanced magnetic resonance imaging. Ex vivo, the tumors were sectioned for both fluorescence microscopy and histology. Nanoparticle uptake and distribution in the tumors were generally heterogeneous. Density of functional blood vessels measured by fluorescence microscopy correlated significantly (p = 0.0056) with nanoparticle accumulation and interestingly, inflow of microbubbles measured with ultrasound also showed a moderate but significant (p = 0.041) correlation with nanoparticle accumulation indicating that both amount of vessels and vessel morphology and perfusion predict nanoparticle accumulation. This indicates that blood vessel characterization using contrast-enhanced ultrasound imaging or other methods could be valuable for patient stratification for treatment with nanomedicines.
Copyright © 2018 Elsevier B.V. All rights reserved.

Entities:  

Keywords:  MRI; Microscopy; Nanoparticles; Tumor characterization; Tumor vasculature; Ultrasound; microCT

Mesh:

Substances:

Year:  2018        PMID: 29684498      PMCID: PMC5972071          DOI: 10.1016/j.jconrel.2018.04.026

Source DB:  PubMed          Journal:  J Control Release        ISSN: 0168-3659            Impact factor:   9.776


  53 in total

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